Increasingly, machines are being equipped with sensors that measure attributes of the machines. The measurements may be analyzed and processed by a system, such as a neural network or a Bayesian network, to ascertain, for example, a current condition of the machine or a current or future need for maintenance or replacement of the machine. Prognostic and Health Management (PHM) and/or Condition-based maintenance (CBM) (collectively “PHM/CBM”) is an increasingly popular field that heavily utilizes such data in order to maintain machines. As the number of machines and number of sensors grows, sensor data becomes substantial. For example, in order to capture frequency components of a signal, the signal is sampled at twice the rate of the highest frequency in the signal, in accordance with the Nyquist-Shannon sampling theorem. The data can then be processed, via a discrete Fourier transform or other suitable transform for example, to extract frequency features from the data. These frequency features can be markers, or indicators, that may be useful in evaluating the condition of the machine, or of particular components of the machine. However, for signals with relatively high frequency components, such as signals associated with vibration analysis, such sampling results in the generation of substantial, if not massive, amounts of data. This data must be stored and then processed, requiring both substantial amounts of storage and substantial processing power.
Accordingly, there is a need for a mechanism by which features can be extracted from signals for use in monitoring and evaluating the health of a machine, that utilizes substantially less data than that generated in accordance with conventional signal sampling theorem, in order to effectively reduce data storage and processor requirements.